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EN
This paper presents a concept of architecture and ontology layouts for the development of multiagent model-based predictive control systems. The presented architecture provides guidelines to simplify the development of agent-based systems and improve their maintainability. The proposed multiagent system (MAS) layout is split into multiple subsystems that include agents dedicated to performing assigned tasks. MAS implementation was prepared which can use provided algorithms and actuators and can react to changes in its environment to reach the best available control quality. An example of MAS based on the proposed architecture is shown in the application of dissolved oxygen (DO) concentration control in a laboratory-activated sludge setup with a biological reactor. For that application, MAS incorporates agent-based controllers from the boundary-based predictive controllers (BBPC) family. Presented experiments prove the flexibility, resilience, and online reconfiguration ability of the proposed multiagent system.
EN
A new method for measurement of sludge blanket height (SBH) based on image analysis is presented. The proposed method uses a histogram back-projection algorithm to distinguish between the settling sludge and supernatant and can be used with sludge possessing different coloring characteristics both in the sludge color and the color of supernatant produced. Individual pixels in the acquired image are compared with a histogram of a representative sludge region. Therefore, the proposed method relies neither on the assumed shape of light intensity profile nor on the dominant sludge or supernatant color. Batch sedimentation tests are presented for different initial sludge concentrations and different background colors to simulate different sludge characteristics. Parameters of a settling velocity function are estimated based on the obtained results. Additionally, an algorithm is proposed that enables the zone settling velocity (ZSV) to be estimated before the batch sedimentation test is completed.
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